浙江农业学报 ›› 2024, Vol. 36 ›› Issue (7): 1481-1491.DOI: 10.3969/j.issn.1004-1524.20240060

• 作物科学 • 上一篇    下一篇

四百八十七份玉米地方种质资源穗部性状的表型多样性

李清超1(), 杨珊1, 张登峰2, 刘建新1, 孙开利1, 吴迅3,*()   

  1. 1.毕节市农业科学研究所,贵州 毕节 551700
    2.中国农业科学院 作物科学研究所,北京 100081
    3.贵州省农业科学院 旱粮研究所,贵州 贵阳 550000
  • 收稿日期:2024-01-11 出版日期:2024-07-25 发布日期:2024-08-05
  • 作者简介:李清超(1984—),男,贵州毕节人,硕士,副研究员,研究方向为作物遗传育种。E-mail: liqingchao-2@163.com
  • 通讯作者: *吴迅,E-mail: wuxunyong@126.com
  • 基金资助:
    贵州省玉米现代农业产业技术体系项目(GZCYTX2023);贵州省重大科技支撑项目(黔科合支撑〔2022〕重点029);贵州省重大科技支撑项目(黔科合支撑〔2022〕重点025);毕节市揭榜挂帅项目(毕科合重大专项〔2023〕5号);贵州省高层次创新型人才项目(毕科人才合字〔2021〕04号)

Phenotypic diversity of ear traits in 487 maize landraces

LI Qingchao1(), YANG Shan1, ZHANG Dengfeng2, LIU Jianxin1, SUN Kaili1, WU Xun3,*()   

  1. 1. Bijie Institute of Agricultural Science, Bijie 551700, Guizhou, China
    2. Institute of Crop Science, Chinese Academy of Agricultural Sciences, Beijing 100081, China
    3. Institute of Drought Crops, Guizhou Academy of Agricultural Sciences, Guiyang 550000, China
  • Received:2024-01-11 Online:2024-07-25 Published:2024-08-05

摘要:

为研究玉米种质资源穗部性状间的相互关系,筛选鉴定优质玉米资源。以贵州、陕西、甘肃、湖北4个省份收集的487份玉米种质资源为研究材料,采用Shannon-Wiener多样性指数、主成分分析和聚类分析等方法对玉米10个穗部性状进行综合评价。结果表明,10个穗部性状变异系数为6.1%~71.0%,表型多样性指数为0.319~2.038。相关分析表明,10个穗部性状间存在广泛的相关性;系统聚类分析将487份资源划分为4个类群,类群Ⅰ有187份种质资源,类群Ⅱ有208份种质资源,类群Ⅲ有48份种质资源,类群Ⅳ有44份种质资源,贵州和陕西的玉米种质资源代表性更强。最终筛选到15份穗部性状综合表现优异的玉米种质资源,为玉米种质资源鉴定评价、种质材料创新和遗传育种提供了参考和基础材料。

关键词: 玉米, 种质资源, 穗部性状, 多样性分析, 系统聚类

Abstract:

To investigate the interrelationship between ear traits in maize germplasm resources and identify high-quality maize germplasm resources, 487 maize germplasm resources collected from four provinces including Guizhou, Shaanxi, Gansu and Hubei were used as study materials, and comprehensive evaluation of 10 ear traits were conducted using methods such as Shannon-Wiener diversity index, principal component analysis, and cluster analysis. The results showed that the coefficient of variation of the 10 ear traits ranged from 6.1% to 71.0%, and the phenotypic diversity indices ranged from 0.319 to 2.038. Correlation analysis revealed widespread correlation between traits. Systematic cluster analysis divided the 487 germplasm resources into four groups: Group Ⅰ included 187 germplasm resources, Group Ⅱ included 208 germplasm resources, Group Ⅲ included 48 germplasm resources, and Group Ⅳ included 44 germplasm resources, with Guizhou and Shaanxi germplasm resources being more representative. In the end, 15 germplasm resources with excellent comprehensive performance in ear traits were selected, which could provide reference and basic materials for maize resource identification, germplasm material innovation, and genetic breeding.

Key words: maize, germplasm resource, ear trait, diversity analysis, systematic cluster

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